Illumination Invariant Face Recognition based on PCA (Eigenface)
Principal Component Analysis (PCA) , single scale Quotient Image, Weberface, homomorphic filtering Illumination Normalization, DCT technique
Human face recognition is one of the research areas in the current era of the research. It is different from other biometric recognition because faces are complex, multidimensional and almost all human faces have a similar construction. One of the most robust face recognition is varying illumination condition in uncontrolled environment is still major challenge. In this thesis we discuss the some normalized methods to solve the common problems in face images, due to a real capture system i.e. lighting variations. We have collected various preprocessing technique suggested by different authors and shown their results. After pre-processing we can use the feature extraction. We have studied a face recognition system using the Principal Component Analysis (PCA) algorithm with Euclidean distance as a classifier. The experimented results are tested on the ORL database and Yale database.
P.T. Chavda, S.Solanki. "Illumination Invariant Face Recognition based on PCA (Eigenface)".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.2, Issue 2, pp.2155-2162, URL :https://rjwave.org/ijedr/papers/IJEDR1402140.pdf
Volume 2 Issue 2
Pages. 2155-2162
